Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

Ezenkwu, Chinedu Pascal; Cannon, San; Ibeke, Ebuka

doi:10.1007/s10661-024-12388-6

COIL Match Maker: a new software application to facilitate COIL collaboration. (2024)
Presentation / Conference Contribution
CRAWFORD, I. and EZENKWU, P. [2024]. COIL Match Maker: a new software application to facilitate COIL collaboration. To be presented at the 6th International virtual exchange conference (IVEC 2024), 21-24 October 2024, [virtual event].

COIL Match Maker is proposed as a new AI-powered software application that is designed to make the process of finding a COIL partner and creating a COIL project faster, simpler and more accessible - regardless of location, prior experience, or availa... Read More about COIL Match Maker: a new software application to facilitate COIL collaboration..

Assessing the research scene of green AI via bibliometric analysis. (2024)
Presentation / Conference Contribution
ABDULMALIK, M.R., IBEKE, E., EZENKWU, C.P. and IWENDI, C. [2024]. Assessing the research scene of green AI via bibliometric analysis. To be published in the Proceedings of the 2024 International conference on advances in communication technology and computer engineering (ICACTCE'24), 29-30 November 2024, Marrakech, Morocco. Lecture notes in networks and systems (LNNS). Cham: Springer [online], (accepted). To be made available from: https://www.springer.com/series/15179

The environmental impact of artificial intelligence (AI) continues to rise as more people embrace the technology. The optimization of AI models to be more efficient, use less energy, and emit low carbon is essential. This bibliometric study presents... Read More about Assessing the research scene of green AI via bibliometric analysis..

Machine learning algorithms for stroke risk prediction leveraging on explainable artificial intelligence techniques (XAI). (2024)
Presentation / Conference Contribution
UGBOMEH, O., YIYE, V., IBEKE, E., EZENKWU, C.P., SHARMA, V. and ALKHAYYAT, A. 2024. Machine learning algorithms for stroke risk prediction leveraging on explainable artificial intelligence techniques (XAI). In Proceedings of the 2024 International conference on electrical, electronics and computing technologies (ICEECT 2024), 29-31 August 2024, Greater Noida, India. Piscataway: IEEE [online], article 10739320. Available from: https://doi.org/10.1109/ICEECT61758.2024.10739320

Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This resea... Read More about Machine learning algorithms for stroke risk prediction leveraging on explainable artificial intelligence techniques (XAI)..

Investigating key contributors to hospital appointment no-shows using explainable AI. (2024)
Presentation / Conference Contribution
YIYE, V., UGBOMEH, O., EZENKWU, C.P., IBEKE, E., SHARMA, V. and ALKHAYYAT, A. 2024. Investigating key contributors to hospital appointment no-shows using explainable AI. In Proceedings of the 2024 International conference on electrical, electronics and computing technologies (ICEECT 2024), 29-31 August 2024, Greater Noida, India. Piscataway: IEEE [online], article 10739123. Available from: https://doi.org/10.1109/ICEECT61758.2024.10739123

The healthcare sector has suffered from wastage of resources and poor service delivery due to the significant impact of appointment no-shows. To address this issue, this paper uses explainable artificial intelligence (XAI) to identify major predictor... Read More about Investigating key contributors to hospital appointment no-shows using explainable AI..

Cost optimisation in offshore wind through procurement data analytics. (2024)
Presentation / Conference Contribution
SHITTU, Q. and EZENKWU, C.P. 2024. Cost optimisation in offshore wind through procurement data analytics. In Arai, K. (eds.) Intelligent computing: proceedings of the 12th Computing conference 2024 (Computing 2024), 11-12 July 2024, London, UK. Lecture notes in networks and systems, 1019. Cham: Springer [online], volume 4, pages 80-98. Available from: https://doi.org/10.1007/978-3-031-62273-1_6

Governments have implemented a variety of national and international efforts to reduce carbon emissions (so as to prevent the damaging effects of climate change on the environment and the global economy) through the execution of several policies, inc... Read More about Cost optimisation in offshore wind through procurement data analytics..

Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement. (2024)
Journal Article
YOUCEFI, M.R., BOUKREDERA, F.S., GHALEM, K., HADJADJ, A. and EZENKWU, C.P. 2024. Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement. Applied intelligence [online], 54(17-18), pages 7659-7673. Available from: https://doi.org/10.1007/s10489-024-05560-5

The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the sc... Read More about Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement..

Assessing the capabilities of ChatGPT in recognising customer intent in a small training data scenario. (2024)
Presentation / Conference Contribution
EZENKWU, C.P., IBEKE, E. and IWENDI, C. 2024. Assessing the capabilities of ChatGPT in recognising customer intent in a small training data scenario. To be presented at the 3rd International conference on advanced communication and intelligent systems (ICACIS 2024), 16-17 May 2024, New Delhi, India.

This study addresses the issue of recognising customer intent when only limited training data is available. The performance of ChatGPT was evaluated in this scenario, and it was found to be better than traditional machine learning algorithms and the... Read More about Assessing the capabilities of ChatGPT in recognising customer intent in a small training data scenario..

Advancing AI with green practices and adaptable solutions for the future. [Article summary] (2024)
Digital Artefact
STARKEY, A. and EZENKWU, C.P. 2024. Advancing AI with green practices and adaptable solutions for the future. [Article summary]. Posted on The Academic [online], 28 March 2024. Available from: https://theacademic.com/ai-green-practices-adaptable-solutions/

Despite AI's achievements, how can its limitations be addressed to reduce computational costs, enhance transparency and pioneer eco-friendly practices?

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies. (2024)
Journal Article
EZENKWU, C.P., CANNON, S. and IBEKE, E. 2024. Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies. Environmental monitoring and assessment [online], 196(3), article number 231. Available from: https://doi.org/10.1007/s10661-024-12388-6

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change... Read More about Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies..

All Outputs (9)